Peste des Petits Ruminants Risk Factors and Space-Time Clusters in Bangladesh

A K M Anisur Rahman; Sk Shaheenur Islam; Md Abu Sufian; Md Hasanuzzaman Talukder; Michael P Ward; Beatriz Martínez-López

doi:10.3389/fvets.2020.572432

Peste des Petits Ruminants Risk Factors and Space-Time Clusters in Bangladesh

Front Vet Sci. 2021 Jan 25:7:572432. doi: 10.3389/fvets.2020.572432. eCollection 2020.

Authors

A K M Anisur Rahman¹, Sk Shaheenur Islam², Md Abu Sufian², Md Hasanuzzaman Talukder³, Michael P Ward⁴, Beatriz Martínez-López⁵

Affiliations

¹ Department of Medicine, Bangladesh Agricultural University, Mymensingh, Bangladesh.
² Department of Livestock Services, Krishi Khamar Sarak, Dhaka, Bangladesh.
³ Department of Parasitology, Bangladesh Agricultural University, Mymensingh, Bangladesh.
⁴ Sydney School of Veterinary Science, The University of Sydney, Camden, NSW, Australia.
⁵ Department of Medicine and Epidemiology, Center for Animal Disease Modeling and Surveillance, School of Veterinary Medicine, University of California, Davis, Davis, CA, United States.

Abstract

Peste des Petits Ruminants (PPR) is endemic in Bangladesh, but its spatial distribution and risk factors have not yet been reported. Using four years of national-level, passive surveillance data (2014 to 2017), in this study we aimed to identify risk factors, create PPR risk maps and describe PPR time-space clusters. We selected PPR case records-mainly based on presumptive diagnosis of small ruminants in subdistrict veterinary hospitals-and sheep and goat population data from all 64 districts of Bangladesh. Peste des Petits Ruminants cumulative incidence per 10,000 animals at risk per district was used to conduct cluster and hotspot analysis and create predictive maps for each year and all 4 years combined. The association between PPR cumulative incidence and hypothesized risk factors-including climatic variables, elevation, road length, river length, railroad length, land cover, and water bodies-was analyzed using a geographically weighted regression model. The total number of PPR cases reported during the study period was 5.2 million. We found that most PPR cases (27.6%) were reported in the monsoon season. The highest and lowest proportions of cases were reported from Rajshahi (36.1%) and Barisal divisions (2.1%), respectively. We identified five space-time clusters, 9 high-high clusters, and 9 hotspots. The predicted cumulative incidences of PPR were persistently higher in north-east, north-west, and south-east parts of Bangladesh. Road length (P = 0.03) was positively associated with PPR incidence in Bangladesh. Results suggest that movement of animals (road length) plays an important role in the epidemiology of PPR in Bangladesh. Along with restriction of animal movement, hotspots and high-high clusters should be targeted first for immunization coverage in Bangladesh and similar PPR endemic countries to achieve eradication.

Keywords: PPR; cluster analysis; goats; risk mapping; risk-based surveillance; sheep.